Related papers: Grain Boundary Segregation Models for High-Entropy…
Grain boundaries (GBs) can critically influence the microstructural evolution and various materials properties. However, a fundamental understanding of GBs in high-entropy alloys (HEAs) is lacking because of the complex couplings of the…
Do high-entropy alloys and ceramics have their grain boundary (GB) counterparts? As the concept of high-entropy grain boundaries (HEGBs) was initially proposed in 2016, this article provides the first complete and rigorous discussion of the…
The control of solute segregation at grain boundaries (GBs) is essential in engineering alloy properties, however the structure-activity relationship of the key parameter-the segregation energies-still remains elusive. Here we propose the…
Segregation to defects, in particular to grain boundaries (GBs), is an unavoidable phenomenon leading to changed material behavior over time. With the increase of available computational power, unbiased quantum-mechanical predictions of…
Computing the grain boundary (GB) counterparts to bulk phase diagrams represents an emerging research direction. Using a classical embrittlement model system Ga-doped Al alloy, this study demonstrates the feasibility of computing…
The discovery of complex concentrated alloys has unveiled materials with diverse atomic environments, prompting the exploration of solute segregation beyond dilute alloys. Data-driven methods offer promising for modeling segregation in such…
High entropy alloys (HEAs) have emerged as a new class of multicomponent materials, which have potential for high temperature applications. Phase stability and creep deformation, two key selection criteria for high temperature materials,…
The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional theory calculations workflow to construct the Grain…
Solute segregation along grain boundaries (GBs) profoundly affects their thermodynamic and kinetic behaviors in polycrystalline materials. Recently, the spectral approach has emerged as a powerful tool to predict GB segregation. However,…
High entropy alloys (HEA) show promise as a new type of high-performance structural material. Their vast degrees of freedom provide for extensive opportunities to design alloys with tailored properties. However, the compositional…
Grain boundary (GB) segregation in magnesium (Mg) substantially influences its mechanical properties and performance. Atomic-scale modelling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at…
Grain boundary (GB) segregation is a powerful approach for optimizing the thermal and mechanical properties of metal alloys. In this study, we report significant GB interstitial segregation in a representative substitutional binary alloy…
While it is known that alloy components can segregate to grain boundaries (GBs), and that the atomic mobility in GBs greatly exceeds the atomic mobility in the lattice, little is known about the effect of GB segregation on GB diffusion.…
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development…
Modeling solute segregation to grain boundaries at near first-principles accuracy is a daunting task, particularly at finite concentrations and temperatures that require accurate assessments of solute-solute interactions and excess…
In alloys, solute segregation at grain boundaries is classically attributed to three driving forces: a high solution enthalpy, a high size mismatch, and a high difference in interfacial energy. These effects are generally cast into a single…
Grain boundary embrittlement occurs when a solute enriches at a grain boundary and lowers its cohesive energy. While grain boundary enrichment is often attributed to equilibrium segregation effects, most models of embrittlement consider…
This study introduces a language transformer-based machine learning model to predict key mechanical properties of high-entropy alloys (HEAs), addressing the challenges due to their complex, multi-principal element compositions and limited…
Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations. High-entropy alloys (HEAs), however, lack chemical long-range order, limiting the…
Grain boundaries (GBs) govern critical properties of polycrystals. Although significant advancements have been made in characterizing minimum energy GBs, real GBs are seldom found in such states, making it challenging to establish…